Long-term Traffic Simulation via Structured Autoregressive Modeling

Interactive traffic simulation is a vital world model for au-tonomous driving. A central challenge in long-horizon simulation is mod-eling sustained multi-agent interactions, which is further exacerbatedby dynamic token cardinality as agents continuously enter and exit thescene. In this work, we propose that the solution lies in the synergybetween the architectural inductive biases and statistical priors of large-scale sequence models, e.g., Large Language Models (LLMs). Our prob-ing experiments reveal that the transferability of attention mechanismsand the distributional consistency between motion tokens and naturallanguage enable small-scale, heavily frozen LLMs to rapidly adapt to traf-fic modeling. Building on this insight, we introduce RosettaSim, a unifiedframework that projects scene topology, agent states, and spawning in-tents into a structured autoregressive stream with variable length, achiev-ing both strong short-term accuracy and stable long-horizon simulationfidelity. Furthermore, evaluating extended rollouts presents yet anotherhurdle, as one-to-one agent correspondence inevitably fades over time.To address this, we introduce Retrieval-based Traffic Evaluation (RTE),which retrieves semantically similar real-world scenarios as context-awarereference anchors. Experiments on the Waymo Open Sim Agent Chal-lenge (WOSAC) demonstrate that RosettaSim achieves state-of-the-artperformance in both short- and long-term simulation. Furthermore, RTEexhibits a stronger correlation with standard metrics (r = 0.83) than ex-isting approaches (r = 0.74), indicating improved alignment with long-horizon simulation fidelity.